mlsanityData to 7 Oct 2026

Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection

arXiv 2311.06222 ↗First posted 10 Nov 2023cs.CVcited by 3

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data. After introducing this new family of generative models, this work proposes and analyses three use cases which demonstrate the potential of diffusion-based approaches for satellite image data. Namely, we tackle cloud removal and inpainting, dataset generation for change-detection tasks, and urban replanning.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 24 of its 64 references that are in the map. First posted on arXiv 10 Nov 2023.

Builds on

24 of 64 references.

References · 24

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  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
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  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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12 more
  1. 2018Supervised learning with quantum enhanced feature spacescited by 654
  2. 2018Barren plateaus in quantum neural network training landscapescited by 624
  3. 2018Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networkscited by 108
  4. 2022Exponential concentration in quantum kernel methodscited by 89
  5. 2020Effect of barren plateaus on gradient-free optimizationcited by 79
  6. 2018Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptionscited by 34
  7. 2021On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classificationcited by 33
  8. 2023UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Seriescited by 28
  9. 2023Quantum algorithms applied to satellite mission planning for Earth observationcited by 17
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  12. 2018Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samplescited by 9

Built on by

Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectivescited by 27
  2. 2024Detecting Out-Of-Distribution Earth Observation Images with Diffusion Modelscited by 14
  3. 2025COP-GEN-Beta: Unified Generative Modelling of COPernicus Imagery Thumbnailscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2024Data Augmentation in Earth Observation: A Diffusion Model Approach
  2. 2023DiffusionSat: A Generative Foundation Model for Satellite Imagery
  3. 2025How to use score-based diffusion in earth system science: A satellite nowcasting example
  4. 2025Environment-Aware Satellite Image Generation with Diffusion Models
  5. 2025Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models
  6. 2025Geospatial Diffusion for Land Cover Imperviousness Change Forecasting
  7. 2025UniTS: Unified Spatio-Temporal Generative Model for Remote Sensing
  8. 2024SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation